Vehicle Make and Model Recognition Using Bag of Expressions

Jamil, Adeel Ahmad; Hussain, Fawad; Yousaf, Muhammad Haroon; Butt, Ammar Mohsin; Velastin, Sergio A.

Abstract

Vehicle make and model recognition (VMMR) is a key task for automated vehicular surveillance (AVS) and various intelligent transport system (ITS) applications. In this paper, we propose and study the suitability of the bag of expressions (BoE) approach for VMMR-based applications. The method includes neighborhood information in addition to visual words. BoE improves the existing power of a bag of words (BOW) approach, including occlusion handling, scale invariance and view independence. The proposed approach extracts features using a combination of different keypoint detectors and a Histogram of Oriented Gradients (HOG) descriptor. An optimized dictionary of expressions is formed using visual words acquired through k-means clustering. The histogram of expressions is created by computing the occurrences of each expression in the image. For classification, multiclass linear support vector machines (SVM) are trained over the BoE-based features representation. The approach has been evaluated by applying cross-validation tests on the publicly available National Taiwan Ocean University-Make and Model Recognition (NTOU-MMR) dataset, and experimental results show that it outperforms recent approaches for VMMR. With multiclass linear SVM classification, promising average accuracy and processing speed are obtained using a combination of keypoint detectors with HOG-based BoE description, making it applicable to real-time VMMR systems.

Más información

Título según WOS: ID WOS:000522448600084 Not found in local WOS DB
Título de la Revista: SENSORS
Volumen: 20
Número: 4
Editorial: MDPI
Fecha de publicación: 2020
DOI:

10.3390/s20041033

Notas: ISI